A similar question arises in our interactions with digital environments. As an analogy, we can think of reinforcement learning (RL) used in RS. The goal of such an RL system is to find an optimal recommendation policy as it continually explores and exploits its state space [1]}. This adaptive process is what drives RL personalization and its various applications in search, ranking, and web-page/app optimization. In the contextual bandit approach, for instance, exploitation translates to recommending items predicted to best match users' preferences, while exploration translates to giving random recommendations hoping to receive more “feedback” [2]}.
